COMPARING THE ACCURACIES OF FORECASTING MODELS FROM THE TIME SERIES DATA OF COVID-19 INFECTION IN NIGERIA

Authors

  • Ibrahim Sa’ad Department of Geography, Adamu Augie College of Education, PMB 1012, Argungu, Kebbi State, Nigeria
  • Azad Rasul Department of Geography, Soran University, Soran 44008, Iraq Scientific Research Centre, Soran University, Delzyan Campus, Soran 44008, Iraq
  • Mohammed S. Ozigis Department of Strategic Space Applications, National Space Research and Development Agency (NASRDA), Abuja, Nigeria
  • Bashir Adamu Department of Geography, Modibbo Adama University of Technology, Yola, Nigeria

DOI:

https://doi.org/10.46827/ejphs.v4i2.106

Keywords:

forecast, covid-19, pandemic, infection, models, vaccine, mortality, accuracy, Nigeria

Abstract

A variety of forecasting models are now fast becoming among the most important application areas in the analyses of recent COVID-19’s future trends as they provide insight to policy-makers about the development of the disease and on healthcare delivery. However, since there is no one-size-fit-all approach in forecasting the future trends of epidemics, the reliability of these approaches is questioned partly due to time series data characteristics (e.g. quality of the data), uncertainty and nature of the modelling approach (e.g. numerical efficiency of the algorithm). This makes comparison of forecasting models necessary in order to provide an evidence-based information with regards to model performance. This study compared the accuracies of ten models in forecasting the number of population to be affected from Coronavirus in Nigeria (specifically for the whole country, as well as for the Federal Capital Territory (FCT) Abuja and Lagos state). Results show that bagged (bootstrap aggregation) model can provide more consistent accurate results (mean absolute error (MAE) of 48 for Nigeria, 32.80 for Lagos and only 13.48 for FCT) than all models assessed in this study. Other models with good performance include exponential smoothing (Nigeria, MAE = 53.65, Lagos = 36.35, FCT = 14.83), structural time series (Nigeria, MAE = 53.62, Lagos = 34.35, FCT = 14.86), ARIMA (Nigeria, MAE = 53.64, Lagos = 36.34, FCT = 14.83), and theta models (Nigeria, MAE = 53.65, Lagos = 36.35, FCT = 14.83). Although forecasting is challenging as models cannot generally provide accurate daily estimates of the COVID-19 infection, daily COVID-19 cases estimated from these models closely reflect the variation in the original data. The study suggests that combining different approaches is of great value to forecasting modelling and therefore, decision makers should treat results from these approaches with caution and base on analysing scenarios.

 

Author Biographies

  • Ibrahim Sa’ad, Department of Geography, Adamu Augie College of Education, PMB 1012, Argungu, Kebbi State, Nigeria
    Dr. Sa’ad Ibrahim, is a Senior Lecturer in the Department of Geography, Adamu Augie College of Education, Argungu, Kebbi Syate, Nigeria.
  • Azad Rasul, Department of Geography, Soran University, Soran 44008, Iraq Scientific Research Centre, Soran University, Delzyan Campus, Soran 44008, Iraq
    Dr. Azad Rasul, is an Assistant Professor, at the Department of Geography, Soran University, Erbil, Iraq.
  • Mohammed S. Ozigis, Department of Strategic Space Applications, National Space Research and Development Agency (NASRDA), Abuja, Nigeria
    Dr. Mohammed Ozigis, is a Scientific Officer in the Department of Strategic Space Agency (National Space Research and Development Agency) Abuja, Nigeria.
  • Bashir Adamu, Department of Geography, Modibbo Adama University of Technology, Yola, Nigeria
    Dr. Bashir Adamu, is a Lecturer at the Department of Geography, Modibbo Adama University Yola, Nigeria.

References

Abdulmajeed, Kabir, Monsuru Adeleke, and Labode Popoola. 2020. Online Forecasting of Covid-19 Cases in Nigeria Using Limited Data. Data in Brief 30:105683. doi: https://doi.org/10.1016/j.dib.2020.105683.

Ahlburg, DA. 1995. Simple versus complex models: evaluation, accuracy, and combining. Math Popul Stud. 5 (3):281-90. doi: doi: 10.1080/08898489509525406. .

Bergmeir, Christoph, Rob J Hyndman, and José M Benítez. 2016. Bagging exponential smoothing methods using STL decomposition and BoxCox transformation. International Journal of Forecasting 32 (2):303-12. doi: https://doi.org/10.1016/j.ijforecast.2015.07.002.

Boseley, S. 2020. New data, new policy: why UKs coronavirus strategy changed.In The Guardian.

Caswell, Hal. 1989. Matrix Population Models- Construction, Analysis, and Interpretation. Sunderland, MA, USA: Sinauer.

Ceylan, Zeynep. 2020. Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment 729:138817. doi: https://doi.org/10.1016/j.scitotenv.2020.138817.

Dantas, Tiago Mendes, and Fernando Luiz Cyrino Oliveira. 2018. Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing.International Journal of Forecasting 34 (4):748-61. doi: https://doi.org/10.1016/j.ijforecast.2018.05.006.

Eker, Sibel. 2020. Validity and usefulness of COVID-19 models.Humanities and Social Sciences Communications 7 (1):54. doi: 10.1057/s41599-020-00553-4.

Embassy of the federal republic of Nigeria. 2020. Geography, Climate and Vegetation, Population & Labour Force. Accessed 22/12/2020. https://www.nigeriabeirut.org/about-nigeria/population-labour-force/.

Gilbert, Marius, Giulia Pullano, Francesco Pinotti, Eugenio Valdano, Chiara Poletto, Pierre-Yves Boëlle, Eric D'Ortenzio, et al. 2020. Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study.The Lancet 395 (10227):871-7. doi: https://doi.org/10.1016/S0140-6736(20)30411-6.

Green, Kesten C., and J. Scott Armstrong. 2015. Simple versus complex forecasting: The evidence. Journal of Business Research 68 (8):1678-85. doi: https://doi.org/10.1016/j.jbusres.2015.03.026.

Haushofer, Johannes, and C. Jessica E. Metcalf. 2020. Which interventions work best in a pandemic? Science 368 (6495):1063. doi: 10.1126/science.abb6144.

Hyndman, R. and Athanasopoulos, G.. Forecasting: principles and practice. http://otexts.org/fpp/.

Ibrahim, Rauf Rauf, and Oluwakemi Hannah Oladipo. 2020. Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure.medRxiv:2020.05.05.20091686. doi: 10.1101/2020.05.05.20091686.

Kalu, Bernard. 2020. COVID-19 in Nigeria: a disease of hunger.The Lancet. Respiratory medicine 8 (6):556-7. doi: 10.1016/s2213-2600(20)30220-4.

Massad, Eduardo, Marcelo N. Burattini, Luis F. Lopez, and Francisco A. B. Coutinho. 2005. Forecasting versus projection models in epidemiology: The case of the SARS epidemics. Medical Hypotheses 65 (1):17-22. doi: https://doi.org/10.1016/j.mehy.2004.09.029.

Massinga Loembé, Marguerite, Akhona Tshangela, Stephanie J. Salyer, Jay K. Varma, Ahmed E. Ogwell Ouma, and John N. Nkengasong. 2020. COVID-19 in Africa: the spread and response. Nature Medicine 26 (7):999-1003. doi: 10.1038/s41591-020-0961-x.

Nigeria Centre for Disease Control. First Case ofCorona Virus Disease Confirmed in Nigeria. Accessed 21/12/2020. https://ncdc.gov.ng/news/227/first-case-of-corona-virus-disease-confirmed-in-nigeria.

Petropoulos, Fotios, and Spyros Makridakis. 2020. Forecasting the novel coronavirus Covid-19.PLOS ONE 15 (3):e0231236. doi: 10.1371/journal.pone.0231236.

Rogers, A. 1995. Population forecasting: do simple models outperform complex models? Math Popul Stud 5 (3):187-202. doi: doi: 10.1080/08898489509525401.

Sample, I. 2020. Coronavirus exposes the problems and pitfalls of modelling. In TheGuardian.

Vespignani, Alessandro, Huaiyu Tian, Christopher Dye, James O. Lloyd-Smith, Rosalind M. Eggo, Munik Shrestha, Samuel V. Scarpino, et al. 2020. Modelling COVID-19.Nature Reviews Physics 2 (6):279-81. doi: 10.1038/s42254-020-0178-4.

Weron, Rafał. 2014. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting 30 (4):1030-81. doi: https://doi.org/10.1016/j.ijforecast.2014.08.008.

Willmott, Cort J., and Kenji Matsuura. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30 (1):79-82.

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Published

2021-12-05

Issue

Vol. 4 No. 2 (2021)

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How to Cite

COMPARING THE ACCURACIES OF FORECASTING MODELS FROM THE TIME SERIES DATA OF COVID-19 INFECTION IN NIGERIA. (2021). European Journal of Public Health Studies, 4(2). https://doi.org/10.46827/ejphs.v4i2.106